Evidence is mounting that the incidence of many diseases such as breast cancer, Alzheimer's disease, and diabetes is a function of both genetic and environmental factors. Moreover, our understanding of the etiology of complex diseases would be improved by a better integration of genetic and epidemiological methods. The objective of this project is to develop potentially improved methods for analysis of disease incidence data. Issues being investigated include: censored age-at-onset outcome data; major genes, polygenes, and shared unmeasured environmental factors; linkage to multiple, polymorphic genetic markers and combined segregation/linkage analysis; genetic heterogeneity and "sporadic" cases; measured time-dependent environmental exposures or covariates; gene-environment interactions and epistasis (gene-gene interactions); mediation of genetic effects through measurable covariates (e.g., hormone levels); associations with candidate genes; and complex ascertainment protocols. The basic model being considered is an extension of the proportional hazards model.